One of China's top product manager communities, gathering 210,000 product managers, operations specialists, designers and other internet professionals; over 800 leading product experts nationwide are signed authors; hosts more than 70 product and growth events each year; all the product manager knowledge you want is right here.
This guide outlines five steps to becoming an AI product manager, covering role differences from traditional PMs, AI industry structure, large model boundaries, PM classifications, competency models, and end-to-end workflow.
This article introduces Duology, an open-source AI skill pack that helps product managers and developers adapt iOS apps for Apple's foldable iPhone Duo by providing layout patterns, state continuity guidance, design recipes, and automated code checks for folded, unfolded, book, and tabletop postures.
This article argues that traditional product team roles are becoming obsolete as AI automates execution, proposing five archetype roles—Prototyper, Builder, Sweeper, Grower, and Maintainer—defined by work patterns and value output, and maps them to product lifecycle stages for dynamic team composition.
This article contrasts traditional product management's deterministic workflow with AI product management's probabilistic nature, outlining core AI PM capabilities — understanding technical boundaries, commercial scenario sensing, and data flywheel awareness — and provides three practical steps for transition: become an AI power user, build no-code agents, and establish custom evaluation benchmarks.
A former B-end product manager shares a validated five-step transition path to AI product management, covering API fundamentals, LangGraph and Dify frameworks, RAG design, tool orchestration, Agent architectures, and a hands-on MVP project that secured a ByteDance offer.
This article reframes user experience as a structured, measurable methodology rather than subjective feeling, covering basic vs. customized experience, C-end vs. B-end differences, quantification metrics, Kano model prioritization, a perception-interest-behavior design framework, and practical principles for both consumer and enterprise products.
Through two case studies, this article outlines the five core responsibilities of product managers: identifying real user problems behind feature requests, scoping requirements with clear boundaries, designing detailed solutions for cross-team collaboration, validating implementations against full business flows, and measuring post-launch impact to drive the next iteration.
This guide outlines five steps to becoming an AI product manager, covering key differences from traditional PMs, AI industry structure types, large model capabilities, PM role classifications, a four-pillar competency model, and the end-to-end AI product workflow.
The article reveals that many AI product managers now function as PDSAs (Product Delivery/Solution Architects), focusing on custom integration, data cleaning, and ROI-driven delivery rather than building standardized AI features, and outlines three career upgrade paths.
This article illustrates the five core responsibilities of product managers — identifying real user problems, scoping requirements, designing actionable solutions, validating end-to-end business flows at launch, and measuring post-launch impact — through two case studies: a real-estate listing notification and a finance reimbursement optimization.
